Triple
T14019708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lünen Town Hall |
E337295
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | City of Lünen |
E67615
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: City of Lünen | Statement: [Lünen Town Hall, ownedBy, City of Lünen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Lünen Context triple: [Lünen Town Hall, ownedBy, City of Lünen]
-
A.
Erftstadt
Erftstadt is a town in the Rhein-Erft district of North Rhine-Westphalia, Germany, located southwest of Cologne and known for its mix of historic villages and suburban residential areas.
-
B.
Tecklenburg
Tecklenburg is a historic small town in North Rhine-Westphalia, Germany, known for its medieval architecture and open-air theater.
-
C.
City of Wesel
The City of Wesel is a historic German town on the Lower Rhine that became an important Reformation and trading center in the early modern period.
-
D.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
E.
Lünen
chosen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3c7cd88190b236382058581740 |
completed | April 14, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd1bb1c48190b5d2b4167c756abf |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 9, 2026, 10:19 p.m.